PROJECT SUMMARY/ABSTRACT Peanut allergy affects 2-5% of US children and 1% of the overall US population. Growing evidence supports a role for gut microbiota in the pathobiology of food allergy. Our group identified differential gut microbiota in children with egg allergy vs. controls, and we identified gut microbiota associated with the later resolution of milk allergy. Gut microbiomes may differ by food allergen. There has been no study of the gut microbiome in well-phenotyped peanut allergic subjects. Our central hypothesis is that gut microbiota shape the development and resolution of peanut allergy. We will leverage longitudinal samples and complementary data from two NIAID-funded cohorts led by us to characterize gut microbiome dynamics in the development and resolution of peanut allergy. Prior cross-sectional studies leave unclear whether dysbiosis precedes or follows food allergy onset. In Aim 1, we will use next-generation sequencing to profile the gut microbiome over time and assess its relationship to the development of peanut allergy. Participants of the CoFAR Observational Study enrolled as infants with high atopic risk. None had peanut allergy at enrollment, while 40.1% now do. Using banked stool from infancy and mid-childhood, we will identify gut microbiome characteristics at infancy that are risk factors for the development of peanut allergy, and longitudinal changes to the gut microbiome that characterize peanut allergy development. In Aim 2, we will identify the relationship between gut microbiome and the resolution of peanut allergy. We will study peanut allergic children enrolled in a desensitization study, of whom peanut allergy is expected to resolve in 32.5%. We will use next-generation sequencing to profile stool collected longitudinally to identify gut microbiome characteristics at baseline, and changes through desensitization, that are associated with peanut allergy resolution. Gut microbiota exert immunologic influence through metabolites (e.g. short chain fatty acids (SCFAs)) they produce. In Aim 3, we will measure targeted SCFA and global metabolome to identify metabolites that are cross-sectionally and longitudinally associated with peanut allergy resolution. We will then apply systems biology methods to (1) decipher causal relationships between microbiome, metabolome, and host, and (2) identify causal key drivers of peanut allergy resolution. The dual gut microbiome and gut metabolome data generated on well-phenotyped peanut allergic subjects by this study, along with parallel host transcriptome data that we will have from U19 AI136053, offer an unprecedented opportunity to develop data-driven mechanistic models for peanut allergy. This study will enable direct progress toward defining the role of the gut microbiome in peanut allergy through an innovative, integrated examination of microbiome, metabolome, and host transcriptome. Results will include the first human data on the gut microbiome in peanut allergy development and resolution, as well as the novel identification of causal key drivers (microbes, metabolites, and host transcripts) of peanut allergy resolution. These results will elucidate mechanisms underlying peanut allergy and highlight therapeutic and biomarker targets.